Drug discovery, a process by which bioactive compounds are identified and preliminarily characterized, is a critical step in the development of treatments for human diseases. Two approaches presently dominate the search for new drugs. The first begins with a screen for compounds that have a desired effect on a cell (e.g., induction of apoptosis), or organism (e.g., inhibition of angiogenesis) as measured in a specific assay. Compounds with the desired activity may then be modified to increase potency, stability, or other properties, and the modified compounds retested in the assay. Thus, a compound that acts as an inhibitor of angiogenesis when tested in a mouse tumor model may be identified, and structurally related compounds synthesized and tested in the same assay. One limitation of this approach is that, often, the mechanism of action and molecular target(s) affected by the compound are unknown, and cannot be determined by the screen. In addition, the assay may provide little information about the specificity of the drug's effect. Finally, the number of compounds that can be screened by assaying biological effects on cells or animals is limited.
In contrast, the second approach to drug screening involves testing numerous compounds for a specific effect on a known molecular target, typically a cloned gene sequence or an isolated enzyme or protein. For example, high-throughput assays can be developed in which numerous compounds can be tested for the ability to change the level of transcription from a specific promoter or the binding of identified proteins. Although the use of high-throughput screens is an extremely powerful methodology for identifying drug candidates, it has limitations. A major drawback is that the assay provides little or no information about the effects of a compound at the cellular or organismal level. These effects must be tested by using the drug in a series of cell biologic and whole animal studies to determine toxicity or side effects in vivo. In fact, analysis of the specificity and toxicity studies of candidate drugs can consume a significant fraction of the drug development process (see, e.g., Oliff, A and S. H. Friend, “Molecular Targets for Drug Development,” in DeVita et al. Cancer: Principles & Practice of Oncology 5th Ed. 1997 Lippincott-Raven Publishers, Philadelphia).
Further, raw data from gene expression analysis are often difficult to coherently interpret. Such measurement technologies typically return numerous genes with altered expression in response to a drug, typically 50–100, possibly up to 1,000 or as few as 10. In the typical case, without more analysis, it is not possible to discern cause and effect from such data alone. The fact that one gene among many has an altered expression in a pair of related biological states yields little or no insight into what caused this change and what the effects of this change are. One is left to ad hoc further experimentation to interpret such gene expression results in terms of biological mechanism. Systematic procedures for guiding the interpretation of such data and such further experimentation, at least in the case of drug target screening, are needed.
Thus, there is a need for improved (e.g., faster and less expensive) methods for characterizing activities and targets of drugs based on effective interpretation of expression data. The present invention provides methods for rapidly characterizing the specificity of candidate drugs and identifying their molecular targets.